{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "if not any(path.endswith('textbook') for path in sys.path):\n",
    "    sys.path.append(os.path.abspath('../../..'))\n",
    "from textbook_utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(ch:wrangling_co2)=\n",
    "# Example: Wrangling CO<sub>2</sub> Measurements from the Mauna Loa Observatory  \n",
    "\n",
    "We saw in {numref}`Chapter %s <ch:data_scope>` that the [National Oceanic and Atmospheric Administration (NOAA)](https://www.noaa.gov/) monitors CO<sub>2</sub> concentrations in the air at the [Mauna Loa Observatory](https://gml.noaa.gov/obop/mlo/). We continue with this example and use it to introduce how to make data-quality checks, handle missing values, transform features, and reshape tables.\n",
    "These data are in the file _data/co2_mm_mlo.txt_. Let's begin by figuring out the formatting, encoding, and size of the source before we load it into a dataframe (see {numref}`Chapter %s <ch:files>`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[51131, 'ascii']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import os\n",
    "import chardet\n",
    "\n",
    "co2_file_path = Path('data') / 'co2_mm_mlo.txt'\n",
    "\n",
    "[os.path.getsize(co2_file_path), \n",
    " chardet.detect(co2_file_path.read_bytes())['encoding']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have found that the file is plain text with ASCII encoding and about 50 KiB in size.\n",
    "Since the file is not particularly large, we should have no trouble\n",
    "loading it into a dataframe, but first we need to determine the file's format.\n",
    "Let's look at the first few lines in the file: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "811"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines = co2_file_path.read_text().split('\\n')\n",
    "len(lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['# --------------------------------------------------------------------',\n",
       " '# USE OF NOAA ESRL DATA',\n",
       " '# ',\n",
       " '# These data are made freely available to the public and the',\n",
       " '# scientific community in the belief that their wide dissemination',\n",
       " '# will lead to greater understanding and new scientific insights.']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the file begins with information about the data source. We should\n",
    "read this documentation before starting our analysis, but sometimes the urge\n",
    "to plunge into the analysis wins over and we just start mucking about and\n",
    "discover properties of the data as we go. So let's quickly find where the\n",
    "actual data values are located:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#',\n",
       " '#            decimal     average   interpolated    trend    #days',\n",
       " '#             date                             (season corr)',\n",
       " '1958   3    1958.208      315.71      315.71      314.62     -1',\n",
       " '1958   4    1958.292      317.45      317.45      315.29     -1',\n",
       " '1958   5    1958.375      317.50      317.50      314.71     -1']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[69:75]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have found that the data begins on the 73rd line of the file. We also spot some relevant characteristics:\n",
    "\n",
    "- The values are separated by whitespace, possibly tabs.\n",
    "- The data line up down the rows. For example, the month appears in seventh to eighth position of each line.\n",
    "- The column headings are split over two lines.\n",
    "\n",
    "We can use `read_csv` to read the data into a `pandas` `DataFrame` and provide arguments to specify that the separators are whitespace, there is no header (we will set our own column names), and to skip the first 72 rows of the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mo</th>\n",
       "      <th>DecDate</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Int</th>\n",
       "      <th>Trend</th>\n",
       "      <th>days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>1958.21</td>\n",
       "      <td>315.71</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.62</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>1958.29</td>\n",
       "      <td>317.45</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.29</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>1958.38</td>\n",
       "      <td>317.50</td>\n",
       "      <td>317.50</td>\n",
       "      <td>314.71</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Yr  Mo  DecDate     Avg     Int   Trend  days\n",
       "0  1958   3  1958.21  315.71  315.71  314.62    -1\n",
       "1  1958   4  1958.29  317.45  317.45  315.29    -1\n",
       "2  1958   5  1958.38  317.50  317.50  314.71    -1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2 = pd.read_csv('data/co2_mm_mlo.txt', \n",
    "                  header=None, skiprows=72, sep='\\s+',\n",
    "                  names=['Yr', 'Mo', 'DecDate', 'Avg', 'Int', 'Trend', 'days'])\n",
    "co2.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have successfully loaded the file contents into a dataframe, and we can see that the granularity of the data is a monthly average CO<sub>2</sub>, from 1958 through 2019. Also, the table shape is 738 by 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since scientific studies tend to have very clean data, it's tempting to jump right in and make a plot to see how CO<sub>2</sub> monthly averages have changed. The field `DecDate` conveniently represents the month and year as a numeric feature, so we can easily make a line plot: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.line(co2, x='DecDate', y='Avg', width=350, height=250,\n",
    "        labels={'DecDate':'Date', 'Avg':'Average monthly CO<sub>2</sub>'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yikes! Plotting the data has uncovered a problem. The four dips in the line plot look odd. What happened here? We can check a few percentiles of the dataframe to see if we can spot the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mo</th>\n",
       "      <th>DecDate</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Int</th>\n",
       "      <th>Trend</th>\n",
       "      <th>days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1958.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1958.21</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>312.66</td>\n",
       "      <td>314.62</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1973.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1973.56</td>\n",
       "      <td>328.59</td>\n",
       "      <td>328.79</td>\n",
       "      <td>329.73</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1988.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1988.92</td>\n",
       "      <td>351.73</td>\n",
       "      <td>351.73</td>\n",
       "      <td>352.38</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2004.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2004.27</td>\n",
       "      <td>377.00</td>\n",
       "      <td>377.00</td>\n",
       "      <td>377.18</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2019.62</td>\n",
       "      <td>414.66</td>\n",
       "      <td>414.66</td>\n",
       "      <td>411.84</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Yr    Mo  DecDate     Avg     Int   Trend  days\n",
       "min  1958.0   1.0  1958.21  -99.99  312.66  314.62  -1.0\n",
       "25%  1973.0   4.0  1973.56  328.59  328.79  329.73  -1.0\n",
       "50%  1988.0   6.0  1988.92  351.73  351.73  352.38  25.0\n",
       "75%  2004.0   9.0  2004.27  377.00  377.00  377.18  28.0\n",
       "max  2019.0  12.0  2019.62  414.66  414.66  411.84  31.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2.describe()[3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time, looking a bit more closely at the range of values, we see that some data have unusual values like `-1` and `-99.99`. If we read the information at the top of the file more carefully, we find that `-99.99` denotes a missing monthly average and `-1` signifies a missing value for the number of days the equipment was in operation that month. Even with relatively clean data, it's a good practice to read the documentation and make a few quality checks before jumping into the analysis stage. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality Checks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's step back for a moment and perform some quality checks. We might confirm that we have the expected number of observations; look for unusual values; and cross-check anomalies that we find to the values in other features. \n",
    "\n",
    "First, we consider the shape of the data. How many rows should we have? From looking at the head and tail of the dataframe, the data appear to be in chronological order, beginning with March 1958 and ending with August 2019.\n",
    "This means we should have $ 12 \\times (2019-1957) - 2 - 4 = 738 $ records, which we can check against the shape of the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(738, 7)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our calculations match the number of rows in the data table. \n",
    "\n",
    "Next, let's check the quality of the features, starting with `Mo`. We expect the values to range from 1 to 12, and each month should have 2019 – 1957 = 62  or 61 instances (since the recordings begin in March of the first year and end in August of the most recent year):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[61, 61, 62, 62, 62, 62, 62, 62, 61, 61, 61, 61]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2[\"Mo\"].value_counts().reindex(range(1,13)).tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, Jan, Feb, Sep, Oct, Nov, and Dec have 61 occurrences and the rest 62. \n",
    "\n",
    "Now let's examine the column called `days` with a histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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m6A9U1KuXBmz1xBJ61s7M+1z9+vVDy1LgKwIuD9u2bbMGDRqAJQsCfm3zX5AaNmyYRRRFncDmzZszflgMYl8R2LJlS/A5rVGjRk6wNG7cOKN6a5R470/gVn6kq/wh3nXXXfbEE0+Y/+3Tiu3bt7eBAwcGxdavX289evSw1atXW82aNYOf3XPPPbud5eWXXx6vdJ19hO1Zr+IO4CMPviFd2XVOFwe/ESJd2XFz6fI+hzxkx837mj8xjTxkx81vO34TzPRJ8+xqL+7SmzZtskxv8MVNIruzc1n1z2mupCvTvlyw0rVo0aJghMtHs6ZPnx4stB87dmyQhcWLFwfrv1jTlV2nLIbSTC9qWWR6UePG9KLGjelFjZtHvf/++9aqVSu9gmoayfRiSOLLj3T5+qzWrVtbs2bNgjVbPrrl1uqvjVizZo317dvX5s2bF5QZMmSItWvXLhCvyjbWdBXfpw/p0nKKdGnckC6NG9KlcUO6dG5IVwi7U0891S677LLS93T5u7umTp0arNeqU6eOnXzyyXbeeedZixYtgppmzZplEydODP7t7/ZyGWvatCnSpffRgoxEurS0IV0aN6RL44Z0adyQLp0b0iWy8/lsnzutaF6WN9KLUIsoDOnSkol0adyQLo0b0qVxQ7p0bkiXzi6ySKYXI0OZmIqQLi0VSJfGDenSuCFdGjekS+eGdOnsIotEuiJDmZiKkC4tFUiXxg3p0rghXRo3pEvnhnTp7CKLRLoiQ5mYipAuLRVIl8YN6dK4IV0aN6RL54Z06ewii0S6IkOZmIqQLi0VSJfGDenSuCFdGjekS+eGdOnsIotEuiJDmZiKkC4tFUiXxg3p0rghXRo3pEvnhnTp7CKLRLoiQ5mYipAuLRVIl8YN6dK4IV0aN6RL54Z06ewii0S6IkOZmIqQLi0VSJfGDenSuCFdGjekS+eGdOnsIotEuiJDmZiKkC4tFUiXxg3p0rghXRo3pEvnhnTp7CKLRLoiQ5mYipAuLRVIl8YN6dK4IV0aN6RL54Z06ewii0S6IkOZmIqQLi0VSJfGDenSuCFdGjekS+eGdOnsIotEuiJDmZiKkC4tFUiXxg3p0rghXRo3pEvnhnTp7CKLRLoiQ5mYipAuLRVIl8YN6dK4IV0aN6RL54Z06ewii0S6IkOZmIqQLi0VSJfGDenSuCFdGjekS+eGdOnsIotEuiJDmZiKkC4tFUiXxg3p0rghXRo3pEvnhnTp7CKLRLoiQ5mYipAuLRVIl8YN6dK4IV0aN6RL54Z06ewii0S6IkOZmIqQLi0VSJfGDenSuCFdGjekS+eGdOnsIotEuiJDmZiKkC4tFUiXxg3p0rghXRo3pEvnhnTp7CKLRLoiQ5mYipAuLRVIl8YN6dK4IV0aN6RL54Z06ewii0S6IkOZmIqQLi0VSJfGDenSuCFdGjekS+eGdOnsIotEuiJDmZiKkC4tFUiXxg3p0rghXRo3pEvnhnTp7CKLRLoiQ5mYipAuLRVIl8YN6dK4IV0aN6RL54Z06ewii0S6IkOZmIqQLi0VSJfGDenSuCFdGjekS+eGdOnsIotEuiJDmZiKkC4tFUiXxg3p0rghXRo3pEvnhnTp7CKLRLoiQ5mYipAuLRVIl8YN6dK4IV0aN6RL54Z06ewii0S6IkOZmIqQLi0VSJfGDenSuCFdGrekS9cHn23TTyyCyL32qJe2FqQrAsBVrQLpqirB5MUjXVpOkC6NG9KlcUO6NG5Jl663P9piw+5drp9cFSIHdj/A+nTcB+mqAsOchyJdOUec9waQLg050qVxQ7o0bkiXxq0QpOu7v3/Wtu/YqZ+gGHlD/05Il8gub2FIV95Q560hpEtDjXRp3JAujRvSpXFDutJzQ7r0PpW3SKQrb6jz1hDSpaFGujRuSJfGDenSuCFdSJfecxIQiXQlIAkRHwLSpQFFujRuSJfGDenSuCFdSJfecxIQiXQlIAkRHwLSpQFFujRuSJfGDenSuCFdSJfecxIQiXQlIAkRHwLSpQFFujRuSJfGDenSuGUiXZu3falXXsXIDzd/YcezkL5SijVKSkpKqsi5YMORroJNXdoDR7q0nCJdGjekS+OGdGncMpGuZ1ZtsHte/LfeQBUiR/3gm9Zr0gKeXqyEIdI1fo7NWfpOFbqZFnpYm6Y245wu1rpJ/Qor2Lx5s7kPN27cWGugmkYhXVrikS6NG9KlcUO6NG6ZSte5M5foDVQhcv6IY5GuEH5IF9JVhY9Y8kKRLi0nSJfGDenSuCFdGjekKz03Xhmh96m8RTK9mDfUeWsI6dJQI10aN6RL44Z0adyQLqRL7zkJiES6EpCEiA8B6dKAIl0aN6RL44Z0adyQLqRL7zkJiES6EpCEiA8B6dKAIl0aN6RL44Z0adyQLqRL7zkJiES6EpCEiA8B6dKAIl0aN6RL44Z0adyQLqRL7zkJiES6EpCEiA8B6dKAIl0aN6RL44Z0adyQLqRL7zkJiES6EpCEiA8B6dKAIl0aN6RL44Z0adyQLqRL7zkJiES6EpCEiA8B6dKAIl0aN6RL44Z0adyQLqRL7zkJiES6EpCEiA8B6dKAIl0aN6RL44Z0adyQLqRL7zkJiES6EpCEiA8B6dKAIl0aN6RL44Z0adyQriKXrhdeeMEaNWpkHTt23O1Mp0yZYoMGDbIGDRrovSfmSKQr5gTkoHmkS4OKdGnckC6NG9KlcUO6ily6rrvuOtt7773tnHPO2eVMd+zYYZ07d7b77rvPvvGNb+i9J+ZIpCvmBOSgeaRLg4p0adyQLo0b0qVxQ7qKVLo+/PBDe+WVV+z++++3Zs2a2Yknnlh6pi5cixYtsrlz59qSJUusTp06eu+JORLpijkBOWge6dKgIl0aN6RL44Z0adyQriKVrrfeessmTJhg8+fPr/AMu3TpYgMGDLDevXvrPScBkUhXApIQ8SEgXRpQpEvjhnRp3JAujRvSVaTSlTqtmTNnWo0aNXabXtS7S7Iika5k5SOKo0G6NIpIl8YN6dK4IV0aN6SryKXLPxglJSVWq1YtvYckOBLpSnByxENDujRwSJfGDenSuCFdGjekq8ily09v/fr19vTTT9s///nP3c521KhRPL0ofnYOa9PUZpzTxVo3qV9hDZs3bw6Et3HjxmIL1TMM6dLyjnRp3JAujRvSpXFDuopculavXm19+vSx1q1bW9euXXcb8Ro7dizSJX52kC4RXEgY0qVxRbo0bkiXxg3p0rghXUUuXTfeeKMtW7bMbrnlFr2HJDiS6cUEJ0c8NKRLA4d0adyQLo0b0qVxQ7qKXLp8+rBVq1Y2dOhQvYckOBLpSnByxENDujRwSJfGDenSuCFdGjekq8il6+GHH7apU6cGa7pq1qyp95KERiJdCU1MFQ4L6dLgIV0aN6RL44Z0adyQriKXrlWrVtnAgQODNV3du3e3pk2b7nLG/qb6evXq6b0n5kikK+YE5KB5pEuDinRp3JAujRvSpXFDuopcuu666y579NFH057lrbfeGnw3Y6FuSFehZi79cSNdWk6RLo0b0qVxQ7o0bkhXkUuX3i0KIxLpKow8ZXOUSFc2tL4qi3Rp3JAujRvSpXFDuopcuvyCsn379rRn2aBBA73nJCAS6UpAEiI+BKRLA4p0adyQLo0b0qVxQ7qKXLquu+46u/nmm9Oe5UsvvbTbOi+9K+U/EunKP/Nct4h0aYSRLo0b0qVxQ7o0bkhXkUvXu+++ax988EHpWfoFxn920003Wa9eveyiiy4Kvpsxn9uOHTts69atFa4lq2xfRceIdOUzc/lpC+nSOCNdGjekS+OGdGnckK4il650p7dkyRLr37+/vfbaa1a3bl2996SJ9MX7w4YNs3vvvdc6depUWmratGk2efLkQLj8Dfmpf3uByvalO0CkK/LUxV4h0qWlAOnSuCFdGjekS+OGdFVT6fJ1Xoccckjw/q79999f7z0VRI4bN85efPHFYITNRapz585BqRUrVtigQYNs7ty51rJlSxsxYoS1bdvWhg8fXum+yg4O6Yo0dYmoDOnS0oB0adyQLo0b0qVxQ7qqoXRt27bNHnroIfvd735nS5cu3e37GPWu9N/IZ555xrp162ZnnXWWjRkzplS6pkyZYhs3bgx+5pu37W/KX7hwoVW2D+mqakYKKx7p0vKFdGnckC6NG9KlcUO6ily6brjhBpsxY8YuZ7l58+bg/76ea/DgwXrPCYk86aST7IorriiVrtGjR1uHDh1swIABQeSGDRuCF7b6C1xdxNLtq1WrVtqWGOnKWfpiqxjp0tAjXRo3pEvjhnRp3JCuIpeulStX2tq1a3c5S39NxJFHHml77LGH3msyiCwvXS54PXv2tH79+gXRPurlU4/Lly+3iy++OO2+1MtbZ86cuVurV199tZ0yfo7NWfpOBkcUbZHD2jS1m/ofYc3SvNDfp3BLSkpysmYu2jNJVm1+E/QHKgr5mxLiIOrMvM/Vr18/juYLtk2XBx/9L/TX5+Q7AX5t81+QGjZsmO+mC749H/hI91Jy/7q+F/71mf3sjpdiOc/5vznGek1eaNt37Mx7+3846wg7/qA9zT+TFW1btmwJPqe5evivcePGGZ1zjRLv/QncykuXj2a1b98++Foi39avX289evSw1atX2+WXX552X+o7I+++++7dztJH0mKVrrOPsD3rVfz0p488+JaLBxUSmO7IDsnFwT90SFd2SF26vM8hD9lx877mT1MjD9lx89uO3wQL+RtNsjvj6Epv2rTJ0t3gA+l6a6OdV02l64RvNE8rXS6r/jnNlXRl2pdDpeuzzz6zWbNmBSNK/m9fvH7aaafZ0UcfHV0vqqCm8tI1ffp0W7dunY0dOzYovXjxYhs5cmSwpquyfZUdJNOLOU1hLJUzvahhZ3pR48b0osaN6UWNm0e9//771qpVq7QVPLNqg507c4neQBUi54841npNWhDLSNcN/TtZn477pD16fzivRYsWlhqIqcJpVim0UulyyTrllFPs7bfftu9+97vWpk0bW7Rokb355pt2ySWXlI46VekI0gSXl641a9ZY3759bd68ecEXcA8ZMsTatWsXiFdl+5CuXGQnuXUiXVpukC6NG9KlcUO6NG5IV3puRSFdt9xyS/Dahueff36X4fM77rjDrrrqKvM1X3Xq1NF7TyWRp556ql122WW7vKfLR9wmTpwYRPn7uyZNmlT6RvzK9qVrhpGunKQu1kqRLg0/0qVxQ7o0bkiXxg3pKnLp8ndh+Tux/vd//3eXM/URMJeeJ5980g488EC99wiRvJFegFaNQpAuLdlIl8YN6dK4IV0aN6SryKXrmmuusWXLltns2bN3OVMf+TrnnHOCl5IW8sJbRrr0D35SI5EuLTNIl8YN6dK4IV0aN6SryKXLF8+fccYZwVf++JOCe++9t7388st2zz33BO/Fmjp1qt5zEhCJdCUgCREfAtKlAUW6NG5Il8YN6dK4IV1FLl1+eo8//rhdeeWV9uGHH5aerYuYv6ah0B/LR7r0D35SI5EuLTNIl8YN6dK4IV0aN6SrGkiXn6J/QP7zn/8E71XxV0ZU9pZ3vSvlPxLpyj/zXLeIdGmEkS6NG9KlcUO6NG5IV5FLl3+59HvvvWcXXHDBLmfqTy361wOlniTUu0+8kUhXvPxz0TrSpVFFujRuSJfGDenSuCFdRS5do0aNsubNmwfvwiq7+UvG/OWoS5YssWbNmum9J+ZIpCvmBOSgeaRLg4p0adyQLo0b0qVxQ7qKXLr8Ow39PVz+Tq6KpCuOV0boXXX3SKQrSprJqAvp0vKAdGnckC6NG9KlcUO6ily6/IWj48aNs/nz5wdruXzz92T5tOJzzz1nDz/8sN5zEhCJdCUgCREfAtKlAUW6NG5Il8YN6dK4IV1FLl1+A+vXr5+tWrXKDjjggODP3/72N/MvjrzxxhvtxBNP1HtOAiKRrgQkIeJDQLo0oEiXxg3p0rghXRo3pKvIpctPb/v27fbnP/8ve2mKAAAgAElEQVTZXnvtNfv4449t3333te9///t26KGH6r0mIZFIV0ISEeFhIF0aTKRL44Z0adyQLo0b0lUNpEvvGsmPRLqSn6NsjxDpypbYf8sjXRo3pEvjVuzS9fZHWzQwGURt//JLq1O7doUl69Sqaa++96mdf8dLGdQUfZH5I461XpMW2PYdO6OvPKTGovjC67xTy3ODSFeegeehOaRLg4x0adyQLo1bsUvX1Y/9wx5Y+o4GpwpRh+/bzM7uth/SVQFDf+tCixYtrGbNmlUgXPXQGiUlJSVVr6Ywa0C6CjNvlR010qXlFOnSuCFdGrfqIF23LFirwalC1HEH7410peGHdFWhY0UVinRFRTI59SBdWi6QLo0b0qVxQ7o0bmFRSNc+aREhXWG9Jw/7ka48QM5zE0iXBhzp0rghXRo3pEvjFhaFdCFdYX0k1v1IV6z4c9I40qVhRbo0bkiXxg3p0riFRSFdSFdYH4l1P9IVK/6cNI50aViRLo0b0qVxQ7o0bmFRSBfSFdZHYt2PdMWKPyeNI10aVqRL44Z0adyQLo1bWBTShXSF9ZFY9yNdseLPSeNIl4YV6dK4IV0aN6RL4xYWhXQhXWF9JNb9SFes+HPSONKlYUW6NG5Il8YN6dK4hUUhXUhXWB+JdT/SFSv+nDSOdGlYkS6NG9KlcUO6NG5hUUgX0hXWR2Ldj3TFij8njSNdGlakS+OGdGnckC6NW1gU0oV0hfWRWPcjXbHiz0njSJeGFenSuCFdGjekS+MWFoV0IV1hfSTW/UhXrPhz0jjSpWFFujRuSJfGDenSuIVFIV1IV1gfiXU/0hUr/pw0jnRpWJEujRvSpXFDujRuYVFIF9IV1kdi3Y90xYo/J40jXRpWpEvjhnRp3JAujVtYFNKFdIX1kVj3I12x4s9J40iXhhXp0rghXRo3pEvjFhaFdCFdYX0k1v1IV6z4c9I40qVhRbo0bkiXxg3p0riFRSFdSFdYH4l1P9IVK/6cNI50aViRLo0b0qVxQ7o0bmFRSBfSFdZHYt2PdMWKPyeNI10aVqRL44Z0adyQLo1bWBTShXSF9ZFY9yNdseLPSeNIl4YV6dK4IV0aN6RL4xYWhXQhXWF9JNb9SFes+HPSONKlYUW6NG5Il8YN6dK4hUUhXUhXWB+JdT/SFSv+nDSOdGlYkS6NG9KlcUO6NG5hUUgX0hXWR2Ldj3TFij8njSNdGlakS+OGdGnckC6NW1gU0oV0hfWRWPcjXbHiz0njSJeGFenSuCFdGjekS+MWFoV0IV1hfSTW/UhXrPhz0jjSpWFFujRuSJfGDenSuIVFVWfp+tPPu9m327dIi+iTTz6xJk2aWM2aNcMw5nR/jZKSkpKctpDgypGuBCdHPDSkSwOHdGnckC6NG9KlcQuLqs7S9eCFR9vK9zamReR9LpfC9ZNu+4elJ9iPdI2fY3OWvpMRrCgLHdamqc04p4u1blK/wmo3b95s7sONGzeOstmirwvp0lKMdGnckC6NG9KlcQuLqu7Sddnc1+zVdz8NwxT5/r6d97Xrzzg8o3qRLqQro45SKIWQLi1TSJfGDenSuCFdGrewKKQL6QrrI7HuZ3oxVvw5aRzp0rAiXRo3pEvjhnRp3MKikC6kK6yPxLof6YoVf04aR7o0rEiXxg3p0rghXRq3sCikC+kK6yOx7ke6YsWfk8aRLg0r0qVxQ7o0bkiXxi0sCulCusL6SKz7ka5Y8eekcaRLw4p0adyQLo0b0qVxC4tCupCusD4S636kK1b8OWkc6dKwIl0aN6RL44Z0adzCopAupCusj8S6H+mKFX9OGke6NKxIl8YN6dK4IV0at7AopAvpCusjse5HumLFn5PGkS4NK9KlcUO6NG5Il8YtLArpQrrC+kis+5GuWPHnpHGkS8OKdGnckC6NG9KlcQuLQrqQrrA+Eut+pCtW/DlpHOnSsCJdGjekS+OGdGncwqKQLqQrrI/Euh/pihV/ThpHujSsSJfGDenSuCFdGrewKKQL6QrrI7HuR7pixZ+TxpEuDSvSpXFDujRuSJfGLSwK6UK6wvpIrPuRrljx56RxpEvDinRp3JAujRvSpXELi0K6kK6wPhLrfqQrVvw5aRzp0rAiXRo3pEvjhnRp3MKikC6kK6yPxLof6YoVf04aR7o0rEiXxg3p0rghXRq3sCikC+kK6yOx7ke6YsWfk8aRLg0r0qVxQ7o0bkiXxi0sCulCusL6SKz7ka5Y8eekcaRLw4p0adyQLo0b0qVxC4tCupCusD4S636kK1b8OWkc6dKwIl0aN6RL44Z0adzCopAupCusj8S6H+mKFX9OGke6NKxIl8YN6dK4IV0at7AopAvpCusjse5HumLFn5PGkS4NK9KlcUO6NG5Il8YtLArpQrrC+kis+5GuWPHnpHGkS8OKdGnckC6NG9KlcQuLQrqQrrA+Eut+pCtW/DlpHOnSsCJdGjekS+OGdGncwqKQLqQrrI9Eun/Hjh22detWa9SoUUb1Il0ZYSqoQkiXli6kS+OGdGnckC6NW1gU0oV0hfWRjPf7zbRjx467lJ80aZL16dMn+Nm0adNs8uTJgXB17dq19N+VNYB0ZYy/YAoiXVqqkC6NG9KlcUO6NG5hUUgX0hXWRzLev27dOjvttNPs2WefLY2pXbu21apVy1asWGGDBg2yuXPnWsuWLW3EiBHWtm1bGz58eKX1I10Z4y+YgkiXliqkS+OGdGnckC6NW1gU0oV0hfWRjPevWrXKRo4caY888shuMVOmTLGNGzfamDFjgn1Lly61oUOH2sKFC5GujAkXR0GkS8sj0qVxQ7o0bkiXxi0sCulCusL6SMb7X3jhBRs2bJidfvrpwWjWCSecEIxm+TZ69Gjr0KGDDRgwIPj/hg0brHv37uai5iNh6TZGujLGXzAFkS4tVUiXxg3p0rghXRq3sCikC+kK6yMZ73eRmj17tjVv3txWrlxpjz32mE2YMMF69+5tgwcPtp49e1q/fv2C+nzUq3PnzrZ8+fLSRfUzZ87cra2rr77aThk/x+YsfSfj44iq4GFtmtpN/Y+wZvUqrnH79u1WUlJidevWjarJalGP3wT9gYp69dKArRYUsj9JZ+Z9rn79+tkHV+MIl4dt27ZZgwYNqjGF7E/dr23+C1LDhg2zD054hC97+f1f3rRbFq7N+5G6dPU/aj/7+ayX8t62Nzj/N8dYr8kLbfuOnXlv/8ELj7bL5iJdOQN/11132RNPPGH+t08rtm/f3gYOHBi0t379euvRo4etXr3aatasGfzs7rvv3u1Yrrjiinil6+wjbM96NSpk5CMPviFd2XUhFwe/ESJd2XFz6fI+hzxkx837mj8xXYzykB2J7Eq7dG3ZsiXjJ82zqz3e0i5d1z+9FunKcxqQrhwDX7RoUTDC5aNZ06dPN19oP3bs2KDVxYsXB+u/WNOV4yQksHqmF7WkML2ocWN6UePG9KLGLSyK6UVGusL6SMb7fX1W69atrVmzZsGaLR/d8t8u/bURa9assb59+9q8efOCMkOGDLF27doF4lXZxpqujPEXTEGkS0sV0qVxQ7o0bkiXxi0sCulCusL6SMb7H330UZs6dWqwXqtOnTp28skn23nnnWctWrQI6pg1a5ZNnDgx+HenTp0CGWvatCnSlTHh4iiIdGl5RLo0bkiXxg3p0riFRSFdSFdYH8l6/6ZNm4J1ADVq7L4WijfSZ42z6AKQLi2lSJfGDenSuOVauv6z6Qtb+q+PtIOrYlSrJvVt3t/X2S0L4llIf3a3/ez8O2JaSD/iWOs1aQEL6SvpQzVKfEVjNd2YXiy+xCNdWk6RLo0b0qVxy4d0XXj3UnvxzfyL16U/PMTe/2wr0qV1DTmKhfQyuvwFIl35Y52vlpAujTTSpXFDujRuSJfGLSyK6UWmF8P6SKz7ka5Y8eekcaRLw4p0adyQLo0b0qVxC4tCupCusD4S636kK1b8OWkc6dKwIl0aN6RL44Z0adzCopAupCusj8S6H+mKFX9OGke6NKxIl8YN6dK4IV0at7AopAvpCusjse5HumLFn5PGkS4NK9KlcUO6NG5Il8YtLArpQrrC+kis+5GuWPHnpHGkS8OKdGnckC6NG9KlcQuLQrqQrrA+Eut+pCtW/DlpHOnSsCJdGjekS+OGdGncwqKQLqQrrI/Euh/pihV/ThpHujSsSJfGDenSuCFdGrewKKQL6QrrI7HuR7pixZ+TxpEuDSvSpXFDujRuSJfGLSwK6UK6wvpIrPuRrljx56RxpEvDinRp3JAujRvSpXELi0K6kK6wPhLrfqQrVvw5aRzp0rAiXRo3pEvjhnRp3MKikC6kK6yPxLof6YoVf04aR7o0rEiXxg3p0rghXRq3sCikC+kK6yOx7ke6YsWfk8aRLg0r0qVxQ7o0bkiXxi0sCulCusL6SKz7ka5Y8eekcaRLw4p0adyQLo0b0qVxC4tCupCusD4S636kK1b8OWkc6dKwIl0aN6RL44Z0adzCopAupCusj8S6H+mKFX9OGke6NKxIl8YN6dK4IV0at7AopAvpCusjse5HumLFn5PGkS4NK9KlcUO6NG5Il8YtLArpQrrC+kis+5GuWPHnpHGkS8OKdGnckC6NG9KlcQuLQrqQrrA+Eut+pCtW/DlpHOnSsCJdGjekS+OGdGncwqKQLqQrrI/Euh/pihV/ThpHujSsSJfGDelKz+3tj7ZUCvXLHTusdq1aGviQqJ0lJTZyzgp78c2PclJ/ZZVe+sND7P3PttotC9bmvW2kC+nKe6fLpkGkKxtahVEW6dLyhHRp3JCuyqWrzx8W2fYdOzW4VYi6bWBXm/jUP5GuKjBUQuePONZ6TVoQS84fvPBou2wu0qXkLW8xSFfeUOetIaRLQ410adyQrnDp+vTz7RrcKkTNHtQN6aoCPzUU6QonV6OkpKQkvFhxlkC6ii+vSJeWU6RL44Z0IV3lCTC9yEhXZVcTpGv8HJuz9B3tiluFqMPaNLUZ53Sx1k3qV1jL5s2bzX24cePGVWil+oUiXVrOkS6NG9KFdCFdXxFgpCv8OoJ0IV3hvaSASiBdWrKQLo0b0oV0IV1IV9/O+9r1Zxye0UUE6UK6MuoohVII6dIyhXRp3JAupAvpQrqQrgyvn6zpyhBUARVDurRkIV0aN6QL6UK6kC6kK8PrJ9KVIagCKoZ0aclCujRuSBfShXQhXUhXhtdPpCtDUAVUDOnSkoV0adyQLqQL6UK6kK4Mr59IV4agCqgY0qUlC+nSuCFdSBfShXQhXRleP5GuDEEVUDGkS0sW0qVxQ7qQLqQL6UK6Mrx+Il0ZgiqgYkiXliykS+OGdCFdSBfShXRleP1EujIEVUDFkC4tWUiXxg3pQrqQLqQL6crw+ol0ZQiqgIohXVqykC6NW9Kla/3GrdqJRRC15Ysddtq054zvXowAZoZVHHfw3nZ2t/3s/DteyjAi2mK8kT6cJy9H5eWo4b2kgEogXVqykC6NW9Kla8lbH9mVj6zUTq6KUZP6HWF9b3oe6aoix2zCka7X7NV3P80GWSRlGenKECMjXRmCKqBiSJeWLKRL41YI0nXGTS9oJ1fFqKeGHYN0VZFhtuFIF9KVbZ/Ja3mkK6+489IY0qVhRro0bkhXem5I11qtU1UhCulCuqrQfXIfinTlnnG+W0C6NOJIl8YN6UK6yhO49IeH2PufbbVbFiBd2qdKi3rwwqPtsrlIl0YvT1FIV55A57EZpEuDjXRp3JAupAvp+ooAC+nDryMspGchfXgvKaASSJeWLKRL44Z0IV1IF9LFQvoMr5+MdGUIqoCKIV1aspAujRvShXQhXUgX0pXh9RPpyhBUARVDurRkIV0aN6QL6UK6kC6kK8PrJ9KVIagCKoZ0aclCujRuSBfShXQhXUhXhtdPpCtDUAVUDOnSkoV0adyQLqQL6UK6kK4Mr59IV4agCqgY0qUlC+nSuCFdSBfShXQhXRleP5GuDEEVUDGkS0sW0qVxy0S6pv/1Da3yKkY1rlfbDmrV2M68eXEVa9LCeTkq7+nSeo4WxXu6NG55jUK68oo7L40hXRpmpEvjlql0XfPEKq2BKkQdvm9TG/WDbyJdVWCohPJy1AW2fcdOBV2VYpCuKuHLTzDSlR/O+WwF6dJoI10aN6SL6UWmF5leZHoxw+sn0pUhqAIqhnRpyUK6NG5IF9KFdCFdSFeG10+kK0NQBVQM6dKShXRp3JAupAvpQrqQrgyvn0hXhqAKqBjSpSUL6dK4IV1IF9KFdCFdGV4/ka4MQRVQMaRLSxbSpXFDupAupAvpQroyvH4iXRmCKqBiSJeWLKRL44Z0IV1IF9KFdGV4/US6MgRVQMWQLi1ZSJfGDelCupAupAvpyvD6iXRlCKqAiiFdWrKQLo0b0oV0IV1IF9KV4fUT6coQVAEVQ7q0ZCFdGjekC+lCupAupCvD6yfSlSGoAiqGdGnJyrV0vf3x57ZzZ4l2cFWMqlnDLHdNl9jOnTutZs1aFR5lgzq1bM7St+3aJ1dX8SyyD+eN9P+0F9/8KHtwVYzgjfS8kb6yLlSjpKQknithFTt2FOFIVxQUk1UH0qXlIx/S1e+m523dp1u1A6xC1Mxzj7SZz79pf139QRVq0UJ/ffzXrUHtWkiXhk+Omj2om018CumSAYqB80cca70mIV1IVxoCSJf4yUpwGNKlJQfp0riFRSFdz9unn28PwxT5fqTrpciZZlIh0hVOqahGunbs2GFbt261Ro0ahZ+5mSFdGWEqqEJIl5YupEvjFhaFdCFdYX0kyv3HHby3nd1tPzv/DqQrSq5hdVXLNV3Tpk2zyZMnB8LVtWvX0n9XBgvpCutKhbcf6dJyhnRp3MKikC6kK6yPRLkf6XrNXn330yiRZlRXtZOuFStW2KBBg2zu3LnWsmVLGzFihLVt29aGDx9eKTCkK6P+VFCFkC4tXUiXxi0sCulCusL6SJT7kS6kK8r+lLauKVOm2MaNG23MmDFBmaVLl9rQoUNt4cKFSFdeMpCcRpAuLRdIl8YtLArpQrrC+kiU+5EupCvK/pS2rtGjR1uHDh1swIABQZkNGzZY9+7dbdWqVVarVsWPcnu5OEe6TjmijQ094SCr7c+zV7D5Y+i+1axZM3KG3uIXO0qsTq2K2468wQoqbNu8YU6aSbp0vf3Rlpycd1ilNWvUsG1f7kybc3+I2ftcZZ+XsDYq2//Fjp32kxl/4+nFqkDMMpZXRvD0YpZdpsrFWUgfjrAoFtIPHjzYevbsaf369QvO2Ee9OnfubMuXLy9dVD9r1qzdaIwbN85OGT8nnFIOSrTZs4H9T9tmtvD1/+Sg9sqrrFWzhv24a1u752//znvb3uD3Dm1tRx3QPCdtJ126nnh1vS15K//vDqpfp5b98Fv72AMvv5MT7mGV9uvS1m5ZuDasWE729+n4NXt0xXs5qTus0g6t97Avvtxpa/+zOaxo5PubNahjJxzSyuYsjSfnv+jZ3v644I3IzyuTCn2NTVzn3XHfZvb2x1vs481fZHKokZbZe4961mn/Pc2vM3FsFx73dbvxmTVxNG39j9ovtnuan/D1Zxye0XkXhXT5tGL79u1t4MCBwUmvX7/eevToYatXry4dKUonXRlRohAEIAABCEAAAhBIQ+D111/PiE1RSNf06dNt3bp1Nnbs2OCkFy9ebCNHjgxd05URoZgK3Xffffbpp5/az3/+85iOoDCbfeqpp2zZsmX229/+tjBPIKajXrJkiT300EM2fvz4mI6gMJtds2aN/f73vze/BrFlTuDDDz+0X/3qVzZ79uzMgygZLAHo3bu3Pfnkk9DIksCPfvQju+OOO2yPPfbIMjLa4kUhXX7h69u3r82bN89at25tQ4YMsXbt2gXiVagb0qVlDunSuCFdGjekS+OGdGnckC6Nm0chXTq7CiN9+nDixInBvk6dOtmkSZOsadOmEbeSv+qQLo010qVxQ7o0bkiXxg3p0rghXRo3pEvnVmlktm+kz9FhRFIt0qVhRLo0bkiXxg3p0rghXRo3pEvjhnTp3KpNJNKlpRrp0rghXRo3pEvjhnRp3JAujRvSpXMjEgIQgAAEIAABCBQkgaJYSF+Q5DloCEAAAhCAAASqFQGkq1qlm5OFAAQgAAEIQCAuAkhXXORpFwIQgAAEIACBakUA6Upouv3rbGrXrm116tRJ6BFyWMVMwF/M6y8RzMV3fxYzN84tPYHNmzdbw4YNrUaN3b/zletdem7btm0LPofcC7L7dFXWp+Lsb0hXdnnMeWnvDP5S10WLFgVt/fSnP7Xhw4dXeKHK+cEUUAPOrWPHjrscsb+rrU+fPgV0Fvk71JUrV5p/Z+mPf/xj+8UvflHa8L///e/g//4ND75de+219r3vfS9/B5bwlj766CMbMWJEcJS33XZb6dFOnjzZpk2bVvr/tm3b2vz58xN+Nvk5PH+i+LrrrrMNGzYEIt+/f3+74IILgsa53qXPwapVq+yqq66yV199NSjUq1cvu+KKK6xBgwbB/eHcc8/dJfjZZ5+1r33ta/lJaoJbcTZXX3116TXMv5fZv2fZ39uZhP6GdCWs8/iF/K9//avdeuuttmXLFjvttNOCrzf6zne+k7AjTdbhuCQ4K7/wpDYfKaxVq1ayDjQBR/Poo48GF3P/1oZjjz3WfvnLX5YelX/t1Le+9a3gWx1czE455RR76aWXCvpFw1Eh95ugf7/rYYcdZl9++aXNnDmztOrLLrvMDjnkkKAP+sbIxFfUvb/tt99+wS9F7777btDnnn76adt///0DceV6V3EPXbFihb399tv2gx/8ILgX/OxnP7N+/foFb1Z3pi71EyZMKA2uV69eVF29oOt55ZVXbPv27dalS5dAsvzrpo4++mg7//zzE9HfkK6Eda8zzzwz+A3Gf6vx7aabbrJ33nknuEmyVf5boY8QPvLII2AKIfD3v/89kCj/3rsmTZqUSldqtPD555+3vfbaK6jFJcMv8ieffHK15/rxxx/bP/7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   "source": [
    "px.histogram(co2, x='days', width=350, height=250,\n",
    "            labels={'days':'Days operational in a month'})"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that a handful of months have averages based on measurements taken on fewer than half the days. In addition, there are nearly 200 missing values. A scatterplot can help us cross-check missing data against the year of the recording:"
   ]
  },
  {
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         ],
         "title": {
          "text": "Year"
         },
         "type": "linear"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -3.6357786357786366,
          33.635778635778635
         ],
         "title": {
          "text": "Days operational in month"
         },
         "type": "linear"
        }
       }
      },
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.scatter(co2, x='Yr', y='days', width=350, height=250,\n",
    "          labels={'Yr':'Year', 'days':'Days operational in month' })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The line along the bottom left of the plot shows us that all of the missing data are in the early years of operation. The number of days of operation of the equipment may not have been collected in the early days. It also appears that there might have been problems with the equipment in the mid- to late '80s. What do we do with these conjectures? We can try to confirm them by looking through documentation about the historical readings. If we are concerned about the impact on the CO<sub>2</sub> averages for records with missing values for the number of days of operation, then a simple solution would be to drop the earliest recordings. However, we would want to delay such action until after we have examined the time trends and assess whether there are any potential problems with the CO<sub>2</sub> averages in those early days.\n",
    "\n",
    "Next, let's return to the `-99.99` values for the average CO<sub>2</sub> measurement and begin our checks with a histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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           "title": {
            "standoff": 15
           },
           "zeroline": false,
           "zerolinecolor": "rgb(36,36,36)"
          }
         }
        },
        "width": 350,
        "xaxis": {
         "anchor": "y",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -105,
          415
         ],
         "title": {
          "text": "Average monthly CO<sub>2</sub>"
         },
         "type": "linear"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          0,
          131.57894736842104
         ],
         "title": {
          "text": "count"
         }
        }
       }
      },
      "image/png": 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e3U5mYM8Uuvbv3y/169fPwKu3fcnqiz7HatWqFXdHvz2YLaOfeV+WFm2Peq5mukoOHpT8xRuj1mvXvJHMGdxdDqlbEndfatoJ6ot+7mdlxZAerOLimzZtGpMktQ7q3WmwVMx0aTarQ4cOMnjwYK+3W7duld69e0tRUZHceuut1R6LCLh06dJKVzlkyBBZv359ud9rRkXBq0ED0q2WhoUCl94UVb21aqmfmdYXha49e/ZI48aNM+3SzV+v/mGak5Njvp+Z1kH1pUmTJglB157ig5L31PoYoUskf/GHvtA191c9pGWj6meEMsWfXbt2ScOGDROGrlj/wEkb6Lrvvvtky5YtMnHiRG8MrFy5UkaPHu2t6Yp2LNqAYXoxfW4nphdtesX0ok1fmF606Yv2KszpxVigi+nF78cK04sVphc3bdok/fv3l4KCAmndurUMHz5c2rVr54FXtGNAl92HTzw9A7riUSu8ukBXeFrH0xLQFY9a4dYFusLVO9bWMh66Lr74YrnlllvK7dM1d+5cmTZtmqeh7t81ffp0icyjRjtWnehkumIdjqmvB3Sl3oOqegB02fQF6LLpC5kuu75kPHRVZw070tsdtEH2DOgKUt3EYwNdiWsX5JlAV5DqJhebTFdy+gV1NtAVlLJl4pLpCkFkR00AXY6EdBwG6HIsqKNwQJcjIQMIA3QFIKqDkECXAxH9QgBdfgrZOQ502fGibE+ALpu+AF02fdFeAV02vQG6QvAF6ApBZEdNAF2OhHQcBuhyLKijcECXIyEDCAN0BSCqg5BAlwMR/UIAXX4K2TkOdNnxgkyXTS/K9grosusR0GXTG6ArBF+ArhBEdtQE0OVISMdhyHQ5FtRROKDLkZABhAG6AhDVQUigy4GIfiGALj+F7BwHuux4QabLphdkuuz7oj0Eumz6BHSF4AvQFYLIjpoAuhwJ6TgMmS7HgjoKR6bLkZABhAG6AhDVQUigy4GIfiGALj+F7BwHuux4QabLphdkuuz7QqbLrkdAVwjeAF0hiOyoCaDLkZCOw5Dpciyoo3BkuhwJGUAYMl0BiOogJNDlQES/EECXn0J2jgNddrwg02XTCzJd9n0h02XXI6ArBG+ArhBEdtQE0OVISMdhyHQ5FtRRODJdjoQMIAyZrgBEdRAS6HIgol8IoMtPITvHgS47XpDpsukFmS77vpDpsusR0BWCN0BXCCI7agLociSk4zBkuhwL6igcmS5HQgYQhkxXAKI6CAl0ORDRLwTQ5aeQneNAlx0vyHTZ9IJMl31fyHTZ9QjoCsEboCsEkR01AXQ5EtJxGDJdjgV1FI5MlyMhAwhDpisAUR2EBLociOgXAujyU8jOcaDLjhdkumx6QabLvi9kuux6BHSF4A3QFYLIjpoAuhwJ6TgMmS7HgjoKR6bLkZABhCHTFYCoDkICXQ5E9AsBdPkpZOc40GXHCzJdNr0g02XfFzJddj0CukLwBugKQWRHTQBdjoR0HIZMl2NBHYUj0+VIyADCkOkKQFQHIYEuByL6hQC6/BSycxzosuMFmS6bXpDpsu8LmS67HgFdIXgDdIUgsqMmgC5HQjoOQ6bLsaCOwpHpciRkAGHIdAUgqoOQQJcDEf1CAF1+Ctk5DnTZ8YJMl00vyHTZ94VMl12PgK4QvAG6QhDZURNAlyMhHYch0+VYUEfhyHQ5EjKAMGS6AhDVQUigy4GIfiGALj+F7BwHuux4QabLphdkuuz7QqbLrkdAVwjeAF0hiOyoCaDLkZCOw5Dpciyoo3BkuhwJGUAYMl0BiOogJNDlQES/EECXn0J2jgNddrwg02XTCzJd9n0h02XXI6ArBG+ArhBEdtQE0OVISMdhyHQ5FtRRODJdjoQMIAyZrgBEdRAS6HIgol8IoMtPITvHgS47XpDpsukFmS77vpDpsusR0BWCN0BXCCI7agLociSk4zBkuhwL6igcmS5HQgYQhkxXAKI6CAl0ORDRLwTQ5aeQneNAlx0vyHTZ9IJMl31fyHTZ9QjoCsEboCsEkR01AXQ5EtJxGDJdjgV1FI5MlyMhAwhDpisAUR2EBLociOgXAujyU8jOcaDLjhdkumx6QabLvi9kuux6BHSF4A3QFYLIjpoAuhwJ6TgMmS7HgjoKR6bLkZABhCHTFYCoDkICXdWIWFJSIvoBXL9+/Uo1Dhw4IPv27ZNGjRrFZAHQFZNMJioBXSZsqNQJoMumL0CXTV/IdNn1Beiqwps77rhDFi1aJMXFxdK5c2eZPHmyHH744V7NmTNnSn5+vgdcPXr0KP13NIuBLrs3QMWeAV02vQK6bPoCdNn0Beiy6wvQVcGbd955R6699lp59dVXpUGDBjJlyhTJzs6WsWPHSmFhoQwdOlQWLlwoLVq0kFGjRknbtm0lLy8vqsNAl90bAOhKD2+ALps+AV02fQG67PoCdFXw5o033pDx48fLiy++KFlZWXLvvffKli1bZNKkSTJjxgzZsWOHjBs3zjtrzZo1MmLECFm+fDnQZXeMx9UzMl1xyRVaZaArNKnjagjoikuuUCuzpitUuWNuDOiqINX+/ftlyJAhomu6TjnlFHn22WflkUcekfbt23vZrk6dOsmgQYO8s7Zt2ya9evWSDRs2eNmw6gqZrpjHY8orAl0pt6DKDgBdNn0Bumz6QqbLri9AVxXePP300zJmzBjvyMUXX+yt6apXr54MGzZMcnNzZeDAgd4xzXp1795d1q1bV7qofsGCBZUijh49WnTasmzR9WK6IL+qhfp2h0vN75l6ouDdsGHDmn+xaXSF+kfQ3r17Y355JY0uLe27umvXLmncuHHaX0dNuwD1Rdce16pVK+5L23tA5LdPvStLi7ZHPTev73FSclAkf/GHUeu1a95I5v7qFGnRICvuvlR3QrRER1Xn6LPdQtm9e7e3dEln0hIpsX421TqofxKlQVmyZIk3lThr1ixPGJ1K1IucPn269+8OHTrI4MGDvSvZunWr9O7dW4qKikoF1IX2FYsuvF+7dm25X+tf7vpBUrdu3TRQJXO6qDemeqOQTbGjQLS3ie30MvN6oo91heFYPwgyT6HUXbH6on/UJwJd+0qyZPTT7zmFrjlXdZdDHT5Wv/pW5M9vfRyTwDf2aec91y0U3flAP/cTha4mTZrEdBlpA12///3vpU2bNnLDDTd4F7Zx40Y5//zzvSnEBx980FvfNXHiRO/YypUrRbNYrOmKaQykRSWmF23axPSiTV+YXrTpi/aqpq/p2vL1Phk86y0p+mxnVBOG5B4jN5//IzNGMb1YwQp9M3H+/Pny0EMPeTQ6Z84cefPNN+WBBx6QTZs2Sf/+/aWgoEBat24tw4cPl3bt2nngFa2wpsvMePftCNDlK1FKKgBdKZHdt1Ggy1eilFUAur6XHuhK2RCMrWFN/U2YMEF0mlFL165dZeTIkd5+XVrmzp0r06ZN8/7drVs3b9qxadOmQFds8pqvBXTZtAjosukL0GXTFzJdP/gCdNkdo+V6pmtIdEG1ruuqWNiRPk1MTKCbQFcCooVwCtAVgsgJNAF0JSBaSKeka6Zr81d7JJal/99+VyK/fmwt04vVjKe0WdMVxP3A9GIQqgYTE+gKRtdkowJdySoYzPlAVzC6uoiaztDV566lckBfi4xSFg7rJTfNLwS6gK7KCgBdLh4h4cQAusLROd5WgK54FQunPtAVjs6JtAJ0fa8a04uJjJ40PwfoSh8DgS6bXgFdNn0Bumz6or0CuoCutNinK4hbCOgKQtVgYgJdweiabFSgK1kFgzkf6ApGVxdRgS6gC+gqcyfpxnW6K31OTo6L+4sYjhQAuhwJ6TgM0OVYUEfhgC5HQgYQBugCuoAuoCuAR4vbkECXWz1dRQO6XCnpNg7Q5VZPl9EsQdcZHVvIjJ+fLM0b+38Di769yEL65EcCby9u3FhORTJdyQ+qICIAXUGomnxMoCt5DYOIAHQFoaqbmNag65re7aXos12+F/eTzq3k3GnLeHvRV6noFRKGrj179qT993qxpivJ0RPi6UBXiGLH0RTQFYdYIVYFukIUO86mLELX4FmrfK9iyag+QJevSv4VokLXXXfdJS1btpQrr7yyUqSTTjrJ+9qdI444wr8VozWALqPGVNEtoMumV0CXTV+ALpu+aK+Aru+9YcuIKsZoddD18ccfy1lnnSWvvfaa9yXU6VqArvRxDuiy6RXQZdMXoMumL0DXD74AXWXG6Nq1a+XWW2+VzZs3S/369eWwww4rPaoPWf2C6e7du8u8efPsjuwYegZ0xSCSkSpAlxEjKnQD6LLpC9Bl0xegC+iqcnpRv9/wvffekzvuuEOaNGkip512WqlStWrVEoWVU045RWrXrm13ZMfQM6ArBpGMVAG6jBgBdNk0okKvgC67NjG9+L03ZLqqGKMffvih7Nq1S7p162Z3BCfRM6ArCfFCPhXoClnwGJsj0xWjUCFXA7pCFjyO5oAuoMt3n64DBw54i/8qllatWolmvtK1AF3p4xzQZdMroMumL0CXTV+0V0AX0FUtdOm2ELfffrs8++yzsnv37kqjePXq1dK0aVO7o9unZ0BX+lgHdNn0Cuiy6QvQZdMXoOsHX5herGKMvvjiizJ+/Hj53e9+J127dq2U1Tr66KMlKyvL7ugGutLWm4odB7psWgl02fQF6LLpC9AFdEXdp2vs2LHSrFkzGT16tN0RnETPyHQlIV7IpwJdIQseY3NAV4xChVwN6ApZ8DiaY3qR6cVqpxfz8/Pl//7v/+See+6JY0ilT1WgK328ArpsegV02fQF6LLpC5kuMl1RM10fffSRXHbZZTJ16lRvi4icnBy7IzmBngFdCYiWolOArhQJ79Ms0GXTF6DLpi9AF9AVFboeffRRue2226odvSykt3tj17SeAV02HQW6bPoCdNn0BegCuqJC1z//+U/59NNPqx29p556qmRnZ9sd3T49I9OVPtYBXTa9Arps+gJ02fQF6AK6okKX3WHrpmdAlxsdw4gCdIWhcvxtAF3xaxbGGUBXGCon1gYL6b/XjS0jqhg/y5Ytk7fffrvakXXdddd5382YrgXoSh/ngC6bXgFdNn0Bumz6QqaLTFfUTNf8+fPlpZdeKlVJP/j+8Y9/yNatW6VPnz7eW40NGjSwO7qZXkxbbyp2HOiyaSXQZdMXoMumL0AX0BX39KLezPfee68sX75c5s2bZ3dkx9AzMl0xiGSkCtBlxIgK3QC6bPoCdNn0BegCuuKGLpXsiy++kJ49e8rrr78uLVu2tDu6yXSlrTdkutLDOqDLpk9Al01fgC6gKyHo0unF3r17y5tvvimHHnqo3dENdKWtN0BXelgHdNn0Ceiy6QvQBXRFha5169ZJUVFRudH71VdfSUFBgTRq1IjpRbv3dY3rGdOLNi0Fumz6AnTZ9AXo+sGX31/QWa45o70Zo3QGr2nTplK7du1A+xQVuh588MFKYKWwdc4558hFF10k7dvbESwRlVjTlYhqqTkH6EqN7n6tAl1+CqXmONCVGt1jaZUtI75XaeYV3aRWLIKJyGkdmkuzhnVjrJ1YNRPQlVjX0+csoCt9vAK6bHoFdNn0Beiy6QuZrh98UegqKNwiz6/fEtWsXh1byP/84uTMgq6dO3d6W0Xs2rVLDj/8cDn66KOlVq1YGdXu4Ae67HpTsWdAl02vgC6bvgBdNn0BuoAu34X0jz32mEyYMKHcCO7evbvcfffdcuSRR9od2TH0DOiKQSQjVYAuI0ZU6AbQZdMXoMumL0AX0BUVut544w258sorZeTIkd7biq1bt5a1a9fKrFmz5MCBA6Kbp6ZzAbrSxz2gy6ZXQJdNX4Aum74AXUBXVOiaMmWKbNy4UWbPnl1uBBcWFsqll14qK1askBYtWtgd3T49A7rSxzqgy6ZXQJdNX4Aum74AXUBXVOgaM2aM5OTkyM0331xuBH/55Zdy6qmnyquvvipHHXVU6KO7pKREdJ1Z48aNJTs7u7R9zb7t27fP284ilgIZYd4FAAAgAElEQVR0xaKSjTpAlw0fKvYC6LLpC9Bl0xegC+iKCl1z586VadOmed+/2KpVK08tBZsZM2bIk08+KStXrgx1ZOseYbfeequ89tpr3qasw4YNk379+nl9mDlzpuTn53vA1aNHj9J/R+sg0BWqfUk1BnQlJV9gJwNdgUmbVGCgKyn5Aj2ZLSO+l5e3F6sYZnv37pWBAwfKhg0bpGPHjt7C+aVLl3o1H374YTnzzDMDHZwVg+uU5umnny4jRowol+HS6c6hQ4fKwoULvenOUaNGSdu2bSUvLy9q/4CuUO1LqjGgKyn5AjsZ6ApM2qQCA11JyRfoyUAX0HUw2ggrLi72YObdd9+V3bt3S5cuXSQ3N1eOOeaYQAdmxeCrVq0Sne585ZVXygGX1tPM244dO2TcuHHeaWvWrPHATL+UO1oBukK1MKnGgK6k5AvsZKArMGmTCgx0JSVfoCcDXUBXtdD16aefSlZWlvfWYtnyzTffyCeffCKdO3cOdHCWDT5nzhx5//33RUFJ/6+74p977rlSr149GTt2rHTq1EkGDRrknbJt2zbp1auXl6GLrPnSFwIqlvPPP9+LVbbs379f9IMk1nVhoQmQ4Q2pJ5p5bdKkSYYrYevydbmB7t+nX59BsaWALsdo1qyZrU7RG/n666+9+yWRvS53fVsiI598R5YWbY+qZF7f46TkoEj+4g+j1jujYwu5und7uWrWKl9nlozqI+dOWyYHNHCUsvCGXnLTU4VS9NnOqPXimV6c8fMTpWm9H9Zv+3Y2gQrKNRXXiccTpk6dOjFVj7qm66677pLNmzfLPffcUy6Ygop+DVBZqImptSQq3XnnnfLQQw9521foVKduW9G1a1cPuHRtl2bfdCpUi2a9dC8x/e7ICDxdf/31lVrXrJl+aXfZoov09a/Esgv0k+g2pzpSQH3R/4L+XixH3c2YMHqvKHjhiy3L1Rf9QyXWDwJbva/ZvdHZI71fEoGu4lp1ZMyCD2KEroOSv7hysqGsuh50ndFerpqdAuj6xclSsH5rTDvS331JZ6lXqzjQgaH3i37uJ+KLdkzXmcdSokKXAk6HDh08qClbNBt0wgknyMsvvxza9y9OnTrV+4ta/69FgW/w4MHy+uuvy/jx471+6s9atm7d6u0rpl/WrZm66grTi7EMERt1mF604UPFXjC9aNMXphdt+qK9Ynrxe2/iyXRlzNcAaXZJ10U999xz5Ubw3/72N7nqqqtk9erVoU0raGZLpwgj0KUZuAsvvFB0rZcu6t+yZYtMnDjR66e+VTl69GjWdNl97sTdM6ArbslCOQHoCkXmuBsBuuKWLLQTgC6gq9oJWp2eGzBggPdfnz59vO9dfPvtt+VPf/qT97NO7YVVFLLOPvtsDwB1Ef8f//hH+fzzz+W2226TTZs2Sf/+/aWgoMBbfzZ8+HBp166dB17RCpmusNxLvh2gK3kNg4gAdAWhavIxga7kNQwqAtAFdEVdFacgM2nSJPniiy9Kx6AuYNes0mGHHRbUuKwy7jPPPFP6PZC6XcWNN95Y+hZlZE8xPbFbt24yffp03ywc0BWqfUk1BnQlJV9gJwNdgUmbVGCgKyn5Aj0Z6IoPuobmHiNXnHqU1C2zEXo0g9ocUj8h/5Rx9AWHoNen+n7htfZeFzBrVkkXqLdp0yalb/ZpX3TX+YYNG1YSlh3pExpraXES0GXTJqDLpi9Al01ftFdAV/zQ1Sqnvjy5arOvqQ8PPkXaNqvMBr4niniJJTPQFUuH07EOma70cQ3osukV0GXTF6DLpi9A1w++xLqQXjNdCl2TFpXf3qmiw53b5Misq3pI0wZ1YzK/fp3yL9kBXTHJllwloCs5/cI8G+gKU+3Y2wK6YtcqzJpAV5hqx9cWma7EMl2xQNeEi7rIrNf/6WvI8HOOlU6ty+/5CHT5ypZ8BaAreQ3DigB0haV0fO0AXfHpFVZtoCsspeNvB+gKFroG3L/C15QXR+QCXb4qBVAB6ApA1IBCAl0BCZtkWKArSQEDOh3oCkhYB2GBLqAr+p7+DgaZ1RBAl1VnKvcL6LLpFdBl0xegy6Yv2iugC+gCusrcn/r9fvo1DTk5OXbv2gzsGdBl03Sgy6YvQJdNX4CuH3wJYiG9rulietHu2Pe+PLviF2EDXTYNA7ps+gJ02fQF6LLpC9AFdMW0T5fd4Ztcz4Cu5PQL82ygK0y1Y28L6IpdqzBrAl1hqh1fW0wvMr3I9CLTi/E9NVJQG+hKgegxNAl0xSBSCqoAXSkQPcYmgS6gC+gCumJ8XKSuGtCVOu2jtQx02fQF6LLpC9OLTC8yvbhxY7m7kzVdNh9WQJdNX4Aum74AXTZ9AbqALqAL6LL7dCrTM6DLpk1Al01fgC6bvgBdQBfQBXTZfToBXea9AbpsWgR02fQF6AK6gC6gy+7TCegy7w3QZdMioMumL0AX0AV0AV12n05Al3lvgC6bFgFdNn0BuoAuoAvosvt0ArrMewN02bQI6LLpC9AFdAFdQJfdpxPQZd4boMumRUCXTV+ALqAL6AK67D6dgC7z3gBdNi0Cumz6AnQBXUAX0GX36QR0mfcG6LJpEdBl0xegC+gCuoAuu08noMu8N0CXTYuALpu+AF1AF9AFdNl9OgFd5r0BumxaBHTZ9AXosgFdr/72TGnWsG65QbJv3z6pW7euZGVllfv9oY3K10t2ZAFdQFeyYyiU89mRPhSZ424E6IpbslBOALpCkTmhRvjC6+9lm3lFNyko3CLPr98SVcehucdIq5z6MmnR+1HrdW6TIxMu6iID7l/h68sreWfKLQvf9a3Xv/uRcmm3I33rxVMB6AK64hkvKasLdKVM+qgNA102fQG6bPpCpstGpkuhq9+9r8vOfd9FHSh/GHgi0OXyVjr22GNlI9DlUtLAYgFdgUmbVGCgKyn5AjsZ6ApM2qQDk+mykekCupIeyvEHALri1yxVZwBdqVI+ertAl01fgC6bvpDpItPF9CKZLrtPpzI9A7ps2gR02fQF6LLpC9AFdAFdQJfdpxPQZd4boMumRUCXTV+ALqAL6AK67D6dgC7z3gBdNi0Cumz6AnQBXUAX0GX36QR0mfcG6LJpEdBl0xegC+gCuoAuu08noMu8N0CXTYuALpu+AF1AF9AFdNl9OgFd5r0BumxaBHTZ9AXoArqALqDL7tMJ6DLvDdBl0yKgy6YvQBfQBXQBXXafTkCXeW+ALpsWAV02fQG6gK60ha79+/d7X05Zq1atUhcPHDgg+qWVjRo1iumOY3PUmGQyUYl9ukzYUKkTQJdNX4Aum74AXUBXWkLXCy+8IMOHD5eXX35Z2rdv77k4c+ZMyc/P94CrR48epf+OdusBXXYfTBV7BnTZ9ArosukL0GXTF6AL6Eo76NLvrbrsssvkyy+/lGeeecaDrsLCQhk6dKgsXLhQWrRoIaNGjZK2bdtKXl5e1DsP6LL7YAK60sMboMumT0CXTV+ALqAr7aDr2muvlZ/+9Kfyhz/8QebOnetB14wZM2THjh0ybtw4z9E1a9bIiBEjZPny5UCX3WdPXD0j0xWXXKFVBrpCkzquhoCuuOQKtTJfeP293DOv6CYFhVvk+fVbouo/NPcYaZVTXyYtej9qvc5tcmTCRV1kwP0rfP18Je9M4QuvfWUSefrpp0WnFh966CHp2bOnPP744x50jR07Vjp16iSDBg3yomzbtk169eolGzZskOzs7Gojk+mKQXQjVYAuI0ZU6AbQZdMXoMumL2S6yHSlTabrk08+kUsvvdSbQmzVqlU56Bo2bJjk5ubKwIEDPUc169W9e3dZt25d6aL6Cy64oNJdWFRUJK+//nql3+sDq+wCfbu3b2b1DF9s+o0v+GJTAZu9SuZ+OZBdT373bJEsLdoe9eLy+h4nJQcPSv7ijVHrndGxhVx9Rnu5avYqX7GWjOoj505bJgdKDkatu+CG02XMU+ul6LOdUevN/MXJUrB+a0yZrpZN6snkgg/8M10/6yIDHogh0zUyV/rd94bs3Pdd1Jh39e8q53ZoIsXFxb76tGzZ0reOVkgL6CopKZHBgwfL6aefLv379/cuTCFKF8937txZpk6dKh06dPDqaNm6dav07t1bFKqysrK83+nbjhXLCSecIB9++GG5X+/du1f0r/cmTZrEJCCVwlFAM1179uyRQw45JJwGaSUmBfRe0T9yDj300JjqUykcBfSD/fPPP5fDDjssnAZpJWYF1Be9XyKfTTGfqAmFfd/JjfPejhG6RPIXl/98q9iWB12928tVs9xB18JhveSm+YX+0JUm04v9Tj4iJotiTdSkBXTt2rVLTj755CovfMqUKfLFF1/Ili1bZOLEiV6dlStXyujRo1nTFdNQSY9KTC/a9InpRZu+ML1o0xftFWu6vveGNV12x2iVPSu7pmvTpk1eBqygoEBat27tbSfRrl07D7yiFdZ0pY/pQJdNr4Aum74AXTZ9Abp+8AXosjtGq4WuefPmeXClRd9knDZtmvfvbt26yfTp06Vp06ZAV5r5Wl13gS6bRgJdNn0Bumz6AnQBXWkxvRjr7cOO9LEqlX71gC6bngFdNn0Bumz6AnQBXTUKuuK9zZhejFex1NUHulKnfbSWgS6bvgBdNn0BuoAuoIsvvLb7dCrTM6DLpk1Al01fgC6bvgBdQBfQBXTZfToBXea9AbpsWgR02fQF6AK6gC6gy+7TCegy7w3QZdMioMumL0AX0AV0AV12n05Al3lvgC6bFgFdNn0BuoAuoAvosvt0ArrMewN02bQI6LLpC9AFdAFdQJfdpxPQZd4boMumRUCXTV+ALqAL6AK67D6dgC7z3gBdNi0Cumz6AnQBXUAX0GX36QR0mfcG6LJpEdBl0xegC+gCuoAuu08noMu8N0CXTYuALpu+AF3pBV3zrztNerQ71OlgArqALqcDKqhgbI4alLLJxQW6ktMvqLOBrqCUTT7u9u3bpXnz5pKVlRV3sB17i2X4vLdladH2qOfm9T1OSg6K5C/+MGq9Mzq2kGt6t5fBs1b59mXJqD5y7rRlckADRykLh/WSm+YXStFnO6PWS4cvvFboeubtT3y10QpTL+kaUz2gC+iKaaCkuhLQlWoHqm4f6LLpC9Bl0xcyXemX6ZpS8IGs2/x11AHVv/uRcveAE2MadEAX0BXTQEl1JaAr1Q4AXTYdqLpXQJddt8h0fe9NumS6gC6H9xJfeO1QzIBDAV0BC5xgeDJdCQoX8GlAV8ACJxEe6AK6ok/QJjG4rJ8KdFl36If+AV02vQK6bPoCdNn0helFpheZXmR60e7TqUzPgC6bNgFdNn0Bumz6AnQBXUAX0GX36QR0mfcG6LJpEdBl0xegC+gCuoAuu08noMu8N0CXTYuALpu+AF1AF9AFdNl9OgFd5r0BumxaBHTZ9AXoArqALqDL7tMJ6DLvDdBl0yKgy6YvQBfQBXQBXXafTkCXeW+ALpsWAV02fQG6gC6gC+iy+3QCusx7A3TZtAjosukL0AV0AV1Al92nE9Bl3hugy6ZFQJdNX4AuoAvoArrsPp2ALvPeAF02LQK6bPoCdAFdQBfQZffpBHSZ9wbosmkR0GXTF6AL6AK6gC67Tyegy7w3QJdNi4Aum74AXUAX0AV02X06AV3mvQG6bFoEdNn0BegCuoAuoMvu0wnoMu8N0GXTIqDLpi9AF9AFdAFddp9OQJd5b4AumxYBXTZ9AbqALqAL6LL7dAK6zHsDdNm0COiy6QvQBXQBXUCX3acT0GXeG6DLpkVAl01fgC6gC+gCuuw+nYAu894AXTYtArps+gJ0AV1AF9Bl9+kEdJn3BuiyaRHQZdMXoAvoSjvo2r9/v2RlZUmdOnUq3VUHDhyQffv2SaNGjWK644499ljZCHTFpFWqK3377beye/duadasWaq7QvtlFAC6bA4HoMumL0AX0JU20LVhwwaZPHmyvPvuu55r5513nowfP14aNGjg/Txz5kzJz8/3gKtHjx6l/4526wFddh9MFXsGdNn0Cuiy6QvQZdMXoAvoShvoKiwslM2bN8v5558ve/bskauvvloGDhwo/fr1Ez02dOhQWbhwobRo0UJGjRolbdu2lby8vKh3HtBl98EEdKWHN0CXTZ+ALpu+AF1AV9pAV8Vb6J577pGdO3fKzTffLDNmzJAdO3bIuHHjvGpr1qyRESNGyPLly4Euu8+euHpGpisuuUKrDHSFJnVcDQFdcckVauXt27dL8+bNvWUy8ZYde4tl+Ly3ZWnR9qin5vU9TkoOiuQv/jBqvTM6tpBrereXwbNW+XZlyag+cu60ZXJAA0cpC4f1kpvmF0rRZzuj1pt5RTcpKNwiz6/fErXe0NxjpFVOfZm06P2o9Tq3yZEJF3WRAfev8L2WV/LOlH73vi47930Xte78606TKQUfyLrNX0et17/7kXL3gBN929UKaQld+kC55JJL5IYbbpC+ffvK2LFjpVOnTjJo0CDvordt2ya9evUSnZLMzs6uVggyXTGNEROVgC4TNlTqBNBl0xegy6Yv2iug63tvgC67Y7RSzx544AEvizV79mypXbu2DBs2THJzc73pRi2a9erevbusW7eudFH9mDFjKsV5+umn5a233ir3+5KSEtH/NC7FjgL6IaIvSuCLHU+0J+qLgldVL7bY6mnm9Ub/UKlbt27mXbjxKy4uLk74fimW2nLTgg9izHQdlPzFG6OqoZmuq89oL1fNdpjpuqGX3PRUDJmuX5wsBeu3xpTpatmknkwu+CDqtcSV6RqZK/3ue8M/03XtaTLl+QzPdBUUFMjtt98uCxYs8FK0WnRasUOHDjJ48GDv561bt0rv3r2lqKioNIW7du3aSoZddtll8t5775X7vb4dqR/uDRs2NH7rZlb39EGlb6Y2adIksy7c+NXqvaJvlebk5BjvaWZ1T2H4m2++kUMOOSSzLjwNrvbrr7/27pdEphd3f3tQRs5/J0boim168ere7eUql9OLsUJXHNOLLZvUl8kFTC+GPryXLVsmmrGaM2eOHH/88aXt33fffbJlyxaZOHGi97uVK1fK6NGjWdMVukPBNcj0YnDaJhOZ6cVk1AvuXKYXg9M22chML36vINOLyY6kgM9fvXq1XHPNNfLII49Ily5dSlurX7++bNq0Sfr37y+aBWvdurUMHz5c2rVr54FXtMKaroBNcxge6HIopsNQQJdDMR2GArociuk4FNAFdEV/FcHxgEs03JAhQ2Tp0qWVTtfpQV23MHfuXJk2bZp3vFu3bjJ9+nRp2rQp0JWo4MbOA7qMGfLv7gBdNn0Bumz6or0CuoCutICuWG4hdqSPRaX0rAN02fQN6LLpC9Bl0xeg6wdfmF60O0YD6xnTi4FJ6zww0OVcUicBgS4nMjoPAnQ5l9RZQDJdZLpqTKYr3rsC6IpXsdTVB7pSp320loEum74AXTZ9IdNFpistN0d1dTsBXa6UDD4O0BW8xom0AHQlolrw5wBdwWucaAtkush0kekqc/fs3btXdE8o9h1K9JESzHlAVzC6JhsV6EpWwWDOB7qC0dVFVKAL6AK6gC4Xz5JAYwBdgcqbcHCgK2HpAj0R6ApU3qSCA11AF9AFdCX1EAnjZKArDJXjbwPoil+zMM4AusJQObE2gC6gC+gCuhJ7eoR4FtAVothxNAV0xSFWiFWBrhDFjrMpoAvoArqArjgfG+FXB7rC1zyWFoGuWFQKvw7QFb7msbYIdAFdQBfQFevzImX1gK6USR+1YaDLpi9Al01ftFdAF9AFdAFddp9Q/+4Z0GXTIqDLpi9Al01fgK4ffGFHertjNLCesU9XYNI6Dwx0OZfUSUCgy4mMzoMAXc4ldRaQTBeZLjJdZLqcPVCCCgR0BaVscnGBruT0C+psoCsoZZOPC3QBXUAX0JX8kyTgCEBXwAInGB7oSlC4gE8DugIWOInwQBfQBXQBXUk8QsI5FegKR+d4WwG64lUsnPpAVzg6J9IK0AV0AV1AVyLPjlDPAbpClTvmxoCumKUKtSLQFarccTVWFXR9/NXemGP8fsF6WVq0PWr9vL7HSclBkfzFH0atd0bHFnJN7/YyeNYq3/aXjOoj505bJgc0cJSycFgvuWl+oRR9tjNqPRbS+0pe8yqwkD59PAW6bHoFdNn0Beiy6Yv2qjrouvzBlb6dnnTxCTL7jX8AXVUo1blNjky4qIsMuH+Fr46v5J0p/e59XXbu+y5q3fnXnSZTCj6QdZu/jlqvf/cj5e4BJ/q2qxVqHdS7M0ML0JU+xgNdNr0Cumz6AnTZ9MUPujZ/tSdqx2df9R9AVzUKAV12x3xpz4CuNDDp310Eumx6BXTZ9AXosukL0PWDL0wv2h2jgfUM6ApMWueBgS7nkjoJCHQ5kdF5EKDLuaTOAkabXiTTVVnmobnHSKuc+jJp0ftRPSDT5WyIBhcI6ApOW9eRgS7XirqJB3S50dF1FKDLtaLu4gFd32tJpsvdmEqbSEBX2lglQJdNr4Aum74AXTZ90V4BXUAXC+nL3J979+6V4uJiycnJsXvXZmDPgC6bpgNdNn0Bumz6AnT94AuZLrtjNLCekekKTFrngYEu55I6CQh0OZHReRCgy7mkzgKS6SLTldGZrr++9U65m+lgyUFRQbKyapX7fb3sbGmZU8/ZjUeg+BQAuuLTK6zaQFdYSsfXDtAVn15h1ga6gK6Mhq6Lpj4lz7z9SdR7rusRTeXBX54CdIX5ZKrQFtCVQvGjNA102fQF6LLpC9OLTC9m/OaoCl1Prfk46h16UttDgK4UP8OArhQbUE3zQJdNX4Aum74AXUAX0AV02X06lekZ0GXTJqDLpi9Al01fgC6gC+gCuuw+nYAu894AXTYtArps+gJ0AV1AF9Bl9+kEdJn3BuiyaRHQZdMXoAvoArqALrtPJ6DLvDdAl02LgC6bvgBdQBfQBXTZfToBXea9AbpsWgR02fQF6AK6gC6gy+7TCegy7w3QZdMioMumL0AX0AV0AV12n05Al3lvgC6bFgFdNn0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stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xtick ticks crisp\" d=\"M0,192v5\" transform=\"translate(225.3,0)\" style=\"stroke: rgb(68, 68, 68); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"xtick ticks crisp\" d=\"M0,192v5\" transform=\"translate(331.07,0)\" style=\"stroke: rgb(68, 68, 68); stroke-opacity: 1; stroke-width: 1px;\"/><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"211.4\" transform=\"translate(119.53,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre; opacity: 1;\">0</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"211.4\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(225.3,0)\">200</text></g><g class=\"xtick\"><text text-anchor=\"middle\" x=\"0\" y=\"211.4\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; 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font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">Average monthly CO​<tspan style=\"font-size:70%\" dy=\"0.3em\">2</tspan><tspan dy=\"-0.21em\">​</tspan></text></g><g class=\"g-ytitle\" transform=\"translate(4.7841796875,0)\"><text class=\"ytitle\" transform=\"rotate(-90,10.215625000000003,100.5)\" x=\"10.215625000000003\" y=\"100.5\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">count</text></g></g></svg>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.histogram(co2, x='Avg', width=350, height=250,\n",
    "            labels={'Avg':'Average monthly CO<sub>2</sub>'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The recorded values are in the 300–400 range, which is what we expect based on our research into CO<sub>2</sub> levels. We also see that there are only a few missing values. Since there aren't many missing values, we can examine all\n",
    "of them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mo</th>\n",
       "      <th>DecDate</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Int</th>\n",
       "      <th>Trend</th>\n",
       "      <th>days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>6</td>\n",
       "      <td>1958.46</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>317.10</td>\n",
       "      <td>314.85</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1958</td>\n",
       "      <td>10</td>\n",
       "      <td>1958.79</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>312.66</td>\n",
       "      <td>315.61</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1964</td>\n",
       "      <td>2</td>\n",
       "      <td>1964.12</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>320.07</td>\n",
       "      <td>319.61</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1964</td>\n",
       "      <td>3</td>\n",
       "      <td>1964.21</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>320.73</td>\n",
       "      <td>319.55</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1964</td>\n",
       "      <td>4</td>\n",
       "      <td>1964.29</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>321.77</td>\n",
       "      <td>319.48</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>1975</td>\n",
       "      <td>12</td>\n",
       "      <td>1975.96</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>330.59</td>\n",
       "      <td>331.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>1984</td>\n",
       "      <td>4</td>\n",
       "      <td>1984.29</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>346.84</td>\n",
       "      <td>344.27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Yr  Mo  DecDate    Avg     Int   Trend  days\n",
       "3    1958   6  1958.46 -99.99  317.10  314.85    -1\n",
       "7    1958  10  1958.79 -99.99  312.66  315.61    -1\n",
       "71   1964   2  1964.12 -99.99  320.07  319.61    -1\n",
       "72   1964   3  1964.21 -99.99  320.73  319.55    -1\n",
       "73   1964   4  1964.29 -99.99  321.77  319.48    -1\n",
       "213  1975  12  1975.96 -99.99  330.59  331.60     0\n",
       "313  1984   4  1984.29 -99.99  346.84  344.27     2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2[co2[\"Avg\"] < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are faced with the question of what to do with the `-99.99` values. We have seen already the problems of leaving these values as is in a line plot. There are several options, and we describe them next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Addressing Missing Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `-99.99`s for average CO<sub>2</sub> levels indicate missing recordings. These interfere with our statistical summaries and plots. It's good to know which values are missing, but we need to do something about them. We might drop those records, replace `-99.99` with `NaN`, or substitute `99.99` with a likely value for the average CO<sub>2</sub>. Let's examine each of these three options."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the table already comes with a substitute value for the `-99.99`.  The column labeled `Int` has values that exactly match those in `Avg`, except when `Avg` is `-99.99`, and then a \"reasonable\" estimate is used instead.\n",
    "\n",
    "To see the effect of each option, let's zoom in on a short time period—say the measurements in 1958—where we know we have two missing values. We can create a time-series plot for the three cases: drop the records with `-99.99`s (left plot); use `NaN` for missing values (middle plot); and substitute an estimate for `-99.99` (right plot). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "remove-input",
     "hide-input"
    ]
   },
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = make_subplots(rows=1, cols=3,\n",
    "    subplot_titles=(\"Missing dropped\", \n",
    "                    \"Missing set to NaN\", \"Missing interpolated\"))\n",
    "\n",
    "co2_small = (co2[co2['Avg'] > 0])[:8] \n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=co2_small['Mo'], y=co2_small['Avg'], \n",
    "               mode='lines+markers'),\n",
    "    row=1, col=1)\n",
    "\n",
    "co2_na = (co2.replace(-99.99, np.NaN))[:10]\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=co2_na['Mo'], y=co2_na['Avg'], mode='lines+markers'),\n",
    "    row=1, col=2)\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=co2_na['Mo'], y=co2_na['Int'], mode='lines+markers'),\n",
    "    row=1, col=3)\n",
    "\n",
    "fig.update_xaxes(tickvals = np.arange(3, 13, 1), row=1, col=1)\n",
    "fig.update_xaxes(tickvals = np.arange(3, 13, 1), row=1, col=2)\n",
    "fig.update_xaxes(tickvals = np.arange(3, 13, 1), row=1, col=3)\n",
    "\n",
    "fig.update_layout(height=250, width=550, showlegend=False, margin=dict(t=30))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we look closely, we can see the difference between each of these plots. The leftmost plot\n",
    "connects dots across a two-month time period, rather than one month.  In the\n",
    "middle plot, the line breaks where the data are missing, and on the right, we can\n",
    "see that months 6 and 10 now have values. In the big picture, since there are\n",
    "only seven values missing from the 738 months, all of these options work.\n",
    "However, there is some appeal to the right plot since the seasonal trends are\n",
    "more cleanly discernible.     \n",
    "\n",
    "The method used to interpolate the CO<sub>2</sub> measurements for the missing values is an averaging process that takes into consideration the month and year. The idea is to reflect both seasonal changes and the long-term trend. This technique is described in greater detail in the documentation at the top of the datafile.\n",
    "\n",
    "These plots have shown the granularity of the data to be monthly measurements, but other granularity options are available to us. We discuss this next. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping the Data Table\n",
    "\n",
    "The CO<sub>2</sub> measurements taken at the Mauna Loa Observatory are also available both daily and hourly. The hourly data has a *finer granularity* than the daily data; and reciprocally, the daily data is *coarser* than the hourly data. \n",
    "\n",
    "Why not always just use the data with the finest granularity available? On a computational level, fine-grained data can become quite large. The Mauna Loa Observatory started recording CO<sub>2</sub> levels in 1958. Imagine how many rows the data table would contain if they provided measurements every single second! But more importantly, we want the granularity of the data to match our research question. Suppose we want to see whether CO<sub>2</sub> levels have risen over the past 50+ years, consistent with global warming predictions. We don’t need a CO<sub>2</sub> measurement every second. In fact, we might well be content with yearly averages where the seasonal patterns are smoothed away. We can aggregate the monthly measurements, changing the granularity to annual averages, and make a plot to display the general trend.\n",
    "We can use *aggregation* to go to a coarser granularity—in `pandas`, we use `.groupby()` and `.agg()`:"
   ]
  },
  {
   "cell_type": "code",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "co2_ann = co2.groupby('Yr')['Int'].mean().reset_index()\n",
    "px.line(co2_ann, x='Yr', y='Int', height=250, width=350,\n",
    "       labels={'Yr':'Year', 'Int':'Average annual CO<sub>2</sub>' })"
   ]
  },
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    "Indeed, we see a rise by nearly 100 ppm of CO<sub>2</sub> since Mauna Loa began recording in 1958."
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    "To recap, after reading the whitespace-separated, plain-text file into a dataframe, we began to check its quality.  We used the scope and context of the data to affirm that its shape matched the range of dates of collection. We confirmed that the values and counts for the month were as expected. We ascertained the extent of missing values in the features, and we looked for connections between missing values and other features. We considered three approaches to handling the missing data: drop records, work with `NaN` values, and impute values to have a full table. And, finally, we changed the granularity of the dataframe by rolling it up from a monthly to an annual average. This change in granularity removed seasonal fluctuations and focused on the long term trend in the level of CO<sub>2</sub> in the atmosphere. The next four sections of this chapter expand on these actions to wrangle data into a form suitable for analysis: quality checks, missing value treatments, transformations, and shape adjustments. We begin with quality checks."
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